Research Area: Machine Learning
Digital technology and social media have brought numerous benefits to human society. TripAdvisor, which runs on user-generated content, provides a platform for travelers to socialize their opinions on several aspects of hotels. Recommendation agents have played an important role for hotel recommendations in the tourism domain. They are valuable tools in e-tourism platforms of travel agencies to help the users in their decision-making process. The recommendation of hotels by multi-criteria Collaborative Filtering (CF) recommender systems is mainly based on their past reviews on several aspects. Hence, recommending the most appropriate hotel to the user is one of the important tasks that a multi-criteria CF needs to do in the e-tourism platform. The aim of this research is to use the multi-criteria ratings in developing a new recommendation method for hotel recommendations in e-tourism platforms. We use supervised and unsupervised machine learning techniques to analysis the customers online reviews. The method is evaluated on the data provided by the travelers via TripAdvisor mobile application. The results of our analysis on the dataset confirm that the use of online reviews in the proposed recommendation agent leads to precise recommendations in TripAdvisor.
Keywords:
Travelers Decision
Social Network Sites
Tripadvisor
Collaborative Filtering
recommender systems
Machine Learning
Deep Learning
Author(s) Name: MehrbakhshNilash,Othman Ibrahim,Elaheh Yadegaridehkordi,Sarminah Samad,Elnaz Akbari and Azar Alizadeh
Journal name: Journal of Computational Science
Conferrence name:
Publisher name: ELSEVIER
DOI: 10.1016/j.jocs.2018.09.006
Volume Information: Volume 28, September 2018, Pages 168-179
